Bibliographic record
Abstract
The immune system fights foreign substances (infectious agents that are perceived to cause harm to the host) that may be found on the skin, in body tissues, the gastrointestinal tract, and in body fluids such as blood. The immune system consists of innate and adaptive systems. While these two systems function in different ways, they both work to protect the host from foreign substances that may detrimentally alter the health of the host (InformedHealth.org [Internet]. Cologne, Germany: Institute for Quality and Efficiency in Health Care (IQWiG) 2006-. The innate and adaptive immune systems. [Updated 2020 Jul 30]). A large part of the immune system's controlling function is focused on regulating the relationship between the host and the microbiota. To aid in this function, the highest number of immune cells reside where foreign substances would be absorbed by the host, the skin, and the GI tract (Belkaid, Y., Hand, T.W. (2014). Role of the microbiota in immunity and inflammation. Cell 157 (1): 121–141. 10.1016/j.cell.2014.03.011). When there is a failure in controlling the dialogue that would prevent or misdirect an immune response, pathologies such as allergies, autoimmune, and inflammatory disorders occur (Belkaid, Y., Hand, T.W. (2014). Role of the microbiota in immunity and inflammation. Cell 157 (1): 121–141. 10.1016/j.cell.2014.03.011).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.068 | 0.032 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".